Application of statistical distribution models to predict health index for condition-based management of transformers
Health Index (HI) is a common tool used for Condition-Based Maintenance (CBM) purpose. It integrates all condition parameter data using a single quantitative index to represent current transformer overall health status. This approach is useful to evaluate the long-term deter...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/85599/1/FK%202020%2055%20-%20ir.pdf http://psasir.upm.edu.my/id/eprint/85599/ |
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| Summary: | Health Index (HI) is a common tool used for Condition-Based Maintenance
(CBM) purpose. It integrates all condition parameter data using a single quantitative
index to represent current transformer overall health status. This approach is useful to evaluate
the long-term deterioration level that may not be viable to be identified by routine inspections
and individual CBM techniques. Besides, it also addresses the interaction between parameter
characteristics and attributes of these CBM techniques. Presently, the existing approach is
normally derived from failure rate data of transformer populations which requires
significant amount of failure data and modelling efforts. Besides, a complete historical
database is required if CBM data is to be used to accurately model the prediction of
transformers’ health condition. This is unfeasible due to poor database management and
insufficient information caused either due to missing or bad quality data. Hence, this project
presents a study on the application of Statistical Distribution Model (SDM) to predict transformers
Health Index (HI) based on the individual condition parameter data in dissolved gas
analysis (DGA), oil quality analysis (OQA) and furanic compound analysis (FCA),
respectively. First, the individual condition parameter data of the transformer population were
categorised based on transformer age from year 1 to 15. Next, the individual condition parameter
data of the transformer population for every age were fitted into probability plot in
order to find the representative distribution models. The distribution parameters for
each of the condition parameter data from year 1 to 15 were computed based on 95% confidence level
of the data population samples. Subsequently, the distribution parameters for each of the
condition parameter data were extrapolated from year 16 to 25 through representative
fitting models. The individual condition parameter data from year 16 to 25 were computed
based on the estimated distribution parameters through inverse cumulative distribution
function (CDF) of the selected distribution models. The future HI of the transformer population
was then estimated based on conventional scoring method. The predicted HI of the transformer
population was compared with the computed HI based on Chi- square test and percentage of
absolute error. Finally, the maintenance costs were estimated based on the combination of HI
conditions and estimated probability of failure (POF) computed from the HI results as maintenance
policy model. It is found that the SDM can be used to predict transformers’ HI. The Chi-square test
for goodness-of-fit reveals that the predicted HI for the transformer population obtained
based on SDM agrees with the computed HI whereby the average percentage of absolute error is
2.7%. The highest percentage of difference between predicted and computed values of HI
is 6.85% along the years. Meanwhile, the accuracy of the HI prediction based on SDM
for the transformer population is 97.83%. The computation method based on HI and POF relationship
curve introduced in this study has inevitably help to reduce in overestimation of the
investment cost as compared to using direct cost translation approach from HI
results of transformer population in each year, hence a realistic capital planning can be
derived as part of asset management strategies. Based on the SDM, it is found that the total
estimated maintenance cost has increased from RM 18.277 million to RM 120.277 million
over the
prediction period of 35 years. |
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